SoroushVahidi/scidocs huggingface dataset:
Numerical methods for the Stokes and Navier-Stokes equations driven by threshold slip boundary conditions
(2016)

In this article, we discuss the numerical solution of the Stokes and Navier-Stokes equations completed by nonlinear slip boundary conditions of friction type in two and three dimensions. To solve the Stokes system, we first reduce the related variational inequality into a saddle point-point problem for a well chosen augmented Lagrangian. To solve this saddle point problem we suggest an alternating direction method of multiplier together with finite element approximations. The solution of the Navier Stokes system combines finite element approximations, time discretization by operator splitting and augmented Lagrangian method. Numerical experiment results for two and three dimensional flow confirm the interest of these approaches.
ETCF: An Ensemble Model for CTR Prediction
(2018)

Online advertising has attracted lots of attention in both academic and industrial domains. Among many realworld advertising systems, click through rate (CTR) prediction plays a central role. With a large volume of user history log and given its various features, it is quite a challenge to fully extract the meaningful information inside that amount of data. What's more, for many machine learning models, in order to achieve the best performance of the CTR prediction, a lot of hyper-parameters need to be tuned, which often costs plenty of time. In this paper, we propose an ensemble model named ETCF, which cascades GBDT with gcForest to tackle the practical problems of CTR prediction and do not need much hyper-parameter tuning work to realize its best performance. Experimental results validate the effective prediction power of ETCF against classical baseline models, and the superiority of GBDT transformed features.
A Computational Model for the Dynamical Learning of Event Taxonomies
(2017)

We present a computational model that can learn event taxonomies online from the continuous sensorimotor information flow perceived by an agent while interacting with its environment. Our model implements two fundamental learning biases. First, it learns probabilistic event models as temporal sensorimotor forward models and event transition models, which predict event model transitions given particular perceptual circumstances. Second, learning is based on the principle of minimizing free energy, which is further biased towards the detection of free energy transients. As a result, the algorithm forms conceptual structures that encode events and event boundaries. We show that event taxonomies can emerge when the algorithm is run on multiple levels of precision. Moreover, we show that generally any type of forward model can be used, as long as it learns sufficiently fast. Finally, we show that the developed structures can be used to hierarchically plan goaldirected behavior by means of active inference.
Heterogeneity and plasticity of T helper cells
(2010)

CD4 T helper (Th) cells play critical roles in adaptive immune responses. They recruit and activate other immune cells including B cells, CD8 T cells, macrophages, mast cells, neutrophils, eosinophils and basophils. Based on their functions, their pattern of cytokine secretion and their expression of specific transcription factors, Th cells, differentiated from naïve CD4 T cells, are classified into four major lineages, Th1, Th2, Th17 and T regulatory (Treg) cells, although other Th lineages may exist. Subsets of the same lineage may express different effector cytokines, reside at different locations or give rise to cells with different fates, whereas cells from different lineages may secrete common cytokines, such as IL-2, IL-9 and IL-10, resulting in massive heterogeneity of the Th cell population. In addition, the pattern of cytokine secretion may switch from that of one lineage toward another under certain circumstances, suggesting that Th cells are plastic. Tregs are also more heterogeneous and plastic than were originally thought. In this review, we summarize recent reports on heterogeneity and plasticity of Th cells, and discuss potential mechanisms and implications of such features that Th cells display.
A Blind Navigation System Using RFID for Indoor Environments
(2015)

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired
Predicting food nutrition facts using pocket-size near-infrared sensor
(2017)

Diet monitoring is one of the most important aspects in preventative health care that aims to reduce various health risks. Manual recording has been a prevalence among all approaches yet it is tedious and often end up with a low adherence rate. Several existing techniques that have been developed to monitor food intake suffer too with accuracy, efficiency, and user acceptance rate. In this paper we propose a novel approach on measuring food nutrition facts, through a pocket-size non-intrusive near-infrared (NIR) scanner. We build efficient regression models that can make quantitative prediction on food nutrition contents, such as energy and carbohydrate. Our extensive experiments on off-the-shelf liquid foods demonstrates the accuracy of these regression models and proves the applicability of using NIR spectra that are collected by small hand-held scanner, on food nutrition prediction.
Sequential Query Expansion using Concept Graph
(2016)

Manually and automatically constructed concept graphs (or semantic networks), in which the nodes correspond to words or phrases and the typed edges designate semantic relationships between words and phrases, have been previously shown to be rich sources of effective latent concepts for query expansion. However, finding good expansion concepts for a given query in large and dense concept graphs is a challenging problem, since the number of candidate concepts that are related to query terms and phrases and need to be examined increases exponentially with the distance from the original query concepts. In this paper, we propose a two-stage feature-based method for sequential selection of the most effective concepts for query expansion from a concept graph. In the first stage, the proposed method weighs the concepts according to different types of computationally inexpensive features, including collection and concept graph statistics. In the second stage, a sequential concept selection algorithm utilizing more expensive features is applied to find the most effective expansion concepts at different distances from the original query concepts. Experiments on TREC datasets of different type indicate that the proposed method achieves significant improvement in retrieval accuracy over state-of-the-art methods for query expansion using concept graphs.
Timing to Perfection: The Biology of Central and Peripheral Circadian Clocks
(2012)

The mammalian circadian system, which is comprised of multiple cellular clocks located in the organs and tissues, orchestrates their regulation in a hierarchical manner throughout the 24 hr of the day. At the top of the hierarchy are the suprachiasmatic nuclei, which synchronize subordinate organ and tissue clocks using electrical, endocrine, and metabolic signaling pathways that impact the molecular mechanisms of cellular clocks. The interplay between the central neural and peripheral tissue clocks is not fully understood and remains a major challenge in determining how neurological and metabolic homeostasis is achieved across the sleep-wake cycle. Disturbances in the communication between the plethora of body clocks can desynchronize the circadian system, which is believed to contribute to the development of diseases such as obesity and neuropsychiatric disorders. This review will highlight the relationship between clocks and metabolism, and describe how cues such as light, food, and reward mediate entrainment of the circadian system.
Deep learning to extract laboratory mouse ultrasonic vocalizations from scalograms
(2017)

We tested two techniques that can assist in the automatic extraction and analysis of laboratory mouse ultrasonic vocalizations. First, we substituted a Morlet-wavelet-based scalogram in place of the commonly used Fourier-based spectrogram. The frequencies in a scalogram are logarithmically scaled, which likely makes it better model a mouse's pitch perception based on the structure of its inner ear. Works that use the linear spectrogram are more likely to overemphasize frequency change at high frequencies, and so might misclassify calls when partitioning them according to frequency modulation. We showed that laboratory mice do indeed modulate their calls more when making high-frequency calls, which we would expect if they perceive pitch logarithmically. Secondly, we used “deep” convolutional neural networks to automatically identify calls within the scalogram. A convolutional neural network performs much like the animal visual cortex, and has enjoyed recent success in computer-vision and related problems. We compared the convolutional network to a conventional fully-connected neural network and the Song filter used by recent papers, and found that our convolutional network identifies calls with greater precision and recall.
Towards Secure Provenance in the Cloud: A Survey
(2015)

Provenance information are meta-data that summarize the history of the creation and the actions performed on an artefact e.g. data, process etc. Secure provenance is essential to improve data forensics, ensure accountability and increase the trust in the cloud. In this paper, we survey the existing cloud provenance management schemes and proposed security solutions. We investigate the current related security challenges resulting from the nature of the provenance model and the characteristics of the cloud and we finally identify potential research directions which we feel necessary t should be covered in order to build a secure cloud provenance for the next generation.
Community detection with partially observable links and node attributes
(2016)

Community detection has been an important task for social and information networks. Existing approaches usually assume the completeness of linkage and content information. However, the links and node attributes can usually be partially observable in many real-world networks. For example, users can specify their privacy settings to prevent non-friends from viewing their posts or connections. Such incompleteness poses additional challenges to community detection algorithms. In this paper, we aim to detect communities with partially observable link structure and node attributes. To fuse such incomplete information, we learn link-based and attribute-based representations via kernel alignment and a co-regularization approach is proposed to combine the information from both sources (i.e., links and attributes). The link-based and attribute-based representations can lend strength to each other via the partial consensus learning. We present two instantiations of this framework by enforcing hard and soft consensus constraint respectively. Experimental results on real-world datasets show the superiority of the proposed approaches over the baseline methods and its robustness under different observable levels.
The Fast-Forward Planning System
(2001)

planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS’00) planning systems competition. Like the well-known HSP system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to HSP and evaluates their benefits in terms of run-time and solution-length behavior.
Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences
(2015)

We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.
Auto-Join: Joining Tables by Leveraging Transformations
(2017)

Traditional equi-join relies solely on string equality comparisons to perform joins. However, in scenarios such as adhoc data analysis in spreadsheets, users increasingly need to join tables whose join-columns are from the same semantic domain but use different textual representations, for which transformations are needed before equi-join can be performed. We developed Auto-Join, a system that can automatically search over a rich space of operators to compose a transformation program, whose execution makes input tables equi-join-able. We developed an optimal sampling strategy that allows Auto-Join to scale to large datasets efficiently, while ensuring joins succeed with high probability. Our evaluation using real test cases collected from both public web tables and proprietary enterprise tables shows that the proposed system performs the desired transformation joins efficiently and with high quality.
Integrated ultra-high impedance front-end for non-contact biopotential sensing
(2011)

Non-contact ECG/EEG electrodes, which operate primarily through capacitive coupling, have been extensively studied for unobtrusive physiological monitoring. Previous implementations using discrete off-the-shelf amplifiers have been encumbered by the need for manually tuned input capacitance neutralization networks and complex DC-biasing schemes. We have designed and fabricated a custom integrated high input impedance (60 fF∥5TΩ), low noise (0.05 fA/√Hz) non-contact sensor front-end specifically to bypass these limitations. The amplifier fully bootstraps both the internal and external parasitic impedances to achieve an input capacitance of 60 fF without neutralization. To ensure DC stability and eliminate the need for external large valued resistances, a low-leakage, on-chip biasing network is included. Stable frequency response is demonstrated below 0.05 Hz even with coupling capacitances as low as 0.5 pF.
Machine learning on sequential data using a recurrent weighted average
(2019)

Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from the previous processing step. To overcome this limitation, we propose a new kind of RNN model that computes a recurrent weighted average (RWA) over every past processing step. Because the RWA can be computed as a running average, the computational overhead scales like that of any other RNN architecture. The approach essentially reformulates the attention mechanism into a stand-alone model. The performance of the RWA model is assessed on the variable copy problem, the adding problem, classification of artificial grammar, classification of sequences by length, and classification of the MNIST images (where the pixels are read sequentially one at a time). On almost every task, the RWA model is found to fit the data significantly faster than a standard LSTM model.
Job shop scheduling
(1997)

Then×m minimum-makespan general job-shop scheduling problem, hereafter referred to as the JSSP, can be described by a set of n jobs{Ji}1≤j≤n which is to be processed on a set of m machines{Mr}1≤r≤m. Each job has a technological sequence of machines to be processed. The processing of job Jj on machineMr is called theoperationOjr. OperationOjr requires the exclusive use of Mr for an uninterrupted durationpjr, its processing time. Ascheduleis a set of completion times for each operation {cjr}1≤j≤n,1≤r≤m that satisfies those constraints. The time required to complete all the jobs is called the makespanL. The objective when solving or optimizing this general problem is to determine t h schedule which minimizesL. An example of a3 × 3 JSSP is given in Table 7.1. The data includes the
Improving the Transcription of Academic Lectures for Information Retrieval
(2013)

Recording university lectures through lecture capture systems is increasingly common, generating large amounts of audio and video data. Transcribing recordings greatly enhances their usefulness by making them easy to search. However, the number of recordings accumulates rapidly, rendering manual transcription impractical. Automatic transcription, on the other hand, suffers from low levels of accuracy, partly due to the special language of academic disciplines, which standard language models do not cover. This paper looks into the use of Wikipedia to dynamically adapt language models for scholarly speech. We propose Ranked Word Correct Rate as a new metric better aligned with the goals of improving transcript search ability and specialist word recognition. The study shows that, while overall transcription accuracy may remain low, targeted language modelling can substantially improve search ability, an important goal in its own right.
Aggregate G-Buffer Anti-Aliasing -Extended Version-
(2016)

We present Aggregate G-Buffer Anti-Aliasing (AGAA), a new technique for efficient anti-aliased deferred rendering of complex geometry using modern graphics hardware. In geometrically complex situations where many surfaces intersect a pixel, current rendering systems shade each contributing surface <italic>at least</italic> once per pixel. As the sample density and geometric complexity increase, the shading cost becomes prohibitive for real-time rendering. Under deferred shading, so does the required framebuffer memory. Our goal is to make high per-pixel sampling rates practical for real-time applications by substantially reducing shading costs and per-pixel storage compared to traditional deferred shading. AGAA uses the rasterization pipeline to generate a compact, pre-filtered geometric representation inside each pixel. We shade this representation at a fixed rate, independent of geometric complexity. By decoupling shading rate from geometric sampling rate, the algorithm reduces the storage and bandwidth costs of a geometry buffer, and allows scaling to high visibility sampling rates for anti-aliasing. AGAA with two aggregates per-pixel generates results comparable to 32<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq1-2586073.gif"/></alternatives></inline-formula> MSAA, but requires 54 percent less memory and is up to 2.6<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq2-2586073.gif"/></alternatives></inline-formula> faster (<inline-formula> <tex-math notation="LaTeX">$-30$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq3-2586073.gif"/></alternatives></inline-formula> percent memory and 1.7 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq4-2586073.gif"/></alternatives></inline-formula> faster for 8<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="crassin-ieq5-2586073.gif"/></alternatives></inline-formula> MSAA).
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
(2011)

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009; Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.
A Generalized Path Integral Control Approach to Reinforcement Learning
(2010)

With the goal to generate more scalable algorithms with high er efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towar ds combining classical techniques from optimal control and dynamic programming with modern learni ng techniques from statistical estimation theory. In this vein, this paper suggests to use the fr amework of stochastic optimal control with path integrals to derive a novel approach to RL with para meterized policies. While solidly grounded in value function estimation and optimal control b ased on the stochastic Hamilton-JacobiBellman (HJB) equations, policy improvements can be transf ormed into an approximation problem of a path integral which has no open algorithmic parameters o ther than the exploration noise. The resulting algorithm can be conceived of as model-based, sem i-model-based, or even model free, depending on how the learning problem is structured. The upd ate equations have no danger of numerical instabilities as neither matrix inversions nor g radient learning rates are required. Our new algorithm demonstrates interesting similarities with previous RL research in the framework of probability matching and provides intuition why the slig htly heuristically motivated probability matching approach can actually perform well. Empirical eva luations demonstrate significant performance improvements over gradient-based policy learnin g a d scalability to high-dimensional control problems. Finally, a learning experiment on a simul ated 12 degree-of-freedom robot dog illustrates the functionality of our algorithm in a complex robot learning scenario. We believe that Policy Improvement withPath Integrals ( PI2) offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL based o n trajectory roll-outs.